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Aiming at the difficulty in setting the support threshold for sequential pattern mining algorithms and improving the effectiveness of the support threshold setting without the guidance of domain experts' experience, an improved SPADE (sequential pattern discovery using equivalence classes) algorithm is proposed. By analyzing the relationship between the number of frequent sequences and the support threshold, the support threshold is dynamically selected. Using the electronic medical record data from a medical centre, the time-series relationship of the drugs taken by hypertension patients was extracted as the drug sequence dataset. By determining the optimal support threshold of the dataset, the frequent sequence set is mined, and the sequence rules are generated from the obtained regular sequences to visualize the sequence rules. The sequence rules of medication for hypertensive patients were combined with the patients' physical indicators for the recommendation. For patients with obstetric hypertension, a combination of nifedipine and captopril is recommended. Through the comparison of the observation group and control group, we study the curative effect of various drugs. The results showed that the total effective rate of the observation group was about 96.6%; compared with the control group, the result indicated that the difference was significant ( P  < 0.05). The comparison of blood pressure levels between the two groups after treatment also showed that the results of the observation group were ideal ( P  < 0.05). In addition, the incidence of postpartum haemorrhage and perinatal complications in the observation group was also significantly reduced ( P  < 0.05). Therefore, the combination of medication for pregnancy hypertension syndrome can effectively improve the treatment effect of the disease and reduce the rate of postpartum haemorrhage and the incidence of perinatal complications.

Study on the Clinical Characteristics and Treatment of Obstetric Hypertension with Sequence Pattern Mining Algorithm